A deep learning based hybrid method for hourly solar radiation forecasting

Lai, Chun Sing; Zhong, Cankun; Pan, Keda; Ng, Wing W. Y.

doi:10.1016/j.eswa.2021.114941

Cited by 56 publications

(10 citation statements)

References 41 publications

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“…Normalized RMSE (nRMSE) is the overall deviations of larger datasets nRMSE can be calculated. where ( ) denotes the mean of the actual solar irradiance [ 27 ].…”

Section: Parameters Affecting Solar Power Forecastingmentioning

confidence: 99%

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

Rahimi

Park

Choi

et al. 2023

J. Electr. Eng. Technol.

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With increasing demand for energy, the penetration of alternative sources such as renewable energy in power grids has increased. Solar energy is one of the most common and well-known sources of energy in existing networks. But because of its non-stationary and non-linear characteristics, it needs to predict solar irradiance to provide more reliable Photovoltaic (PV) plants and manage the power of supply and demand. Although there are various methods to predict the solar irradiance. This paper gives the overview of recent studies with focus on solar irradiance forecasting with ensemble methods which are divided into two main categories: competitive and cooperative ensemble forecasting. In addition, parameter diversity and data diversity are considered as competitive ensemble forecasting and also preprocessing and post-processing are as cooperative ensemble forecasting. All these ensemble forecasting methods are investigated in this study. In the end, the conclusion has been drawn and the recommendations for future studies have been discussed.

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“…Normalized RMSE (nRMSE) is the overall deviations of larger datasets nRMSE can be calculated. where ( ) denotes the mean of the actual solar irradiance [ 27 ].…”

Section: Parameters Affecting Solar Power Forecastingmentioning

confidence: 99%

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

Rahimi

Park

Choi

et al. 2023

J. Electr. Eng. Technol.

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“…As reviewed in [34], there are many feature selection methods. We conducted comparison experiments between our MVFS and several typical feature selection algorithms, namely Fisher score (F-Score), mutual information (MI), joint mutual information (JMI), joint mutual information maximization (JMIM), ReliefF, Hilbert-Schmidt independence criterion lasso (HSIC Lasso) [35] and recursive feature elimination (RFE).…”

Section: Comparison Feature Algorithmsmentioning

confidence: 99%

Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection

et al. 2021

Self Cite

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Fault-cause identification plays a significant role in transmission line maintenance and fault disposal. With the increasing types of monitoring data, i.e., micrometeorology and geographic information, multiview learning can be used to realize the information fusion for better fault-cause identification. To reduce the redundant information of different types of monitoring data, in this paper, a hierarchical multiview feature selection (HMVFS) method is proposed to address the challenge of combining waveform and contextual fault features. To enhance the discriminant ability of the model, an ε-dragging technique is introduced to enlarge the boundary between different classes. To effectively select the useful feature subset, two regularization terms, namely l2,1-norm and Frobenius norm penalty, are adopted to conduct the hierarchical feature selection for multiview data. Subsequently, an iterative optimization algorithm is developed to solve our proposed method, and its convergence is theoretically proven. Waveform and contextual features are extracted from yield data and used to evaluate the proposed HMVFS. The experimental results demonstrate the effectiveness of the combined used of fault features and reveal the superior performance and application potential of HMVFS.

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“…Recently, a hybrid model combining the two models was also developed [13][14][15][16]. Some studies on the hybrid model have combined single machine-learning models.…”

Section: Introductionmentioning

confidence: 99%

Solar Radiation Forecasting Based on the Hybrid CNN-CatBoost Model

Kim

Park

Park³

et al. 2023

IEEE Access

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The renewable energy industry is rapidly expanding due to environmental pollution from fossil fuels and continued price hikes. In particular, the solar energy sector accounts for about 48.7% of renewable energy, at the highest production ratio. Therefore, climate prediction is essential because solar power is affected by weather and climate change. However, solar radiation, which is most closely related to solar power, is not currently predicted by the Korea Meteorological Administration; therefore, solar radiation prediction technology is needed. In this study, we predict solar radiation using extra-atmospheric solar radiation and three weather variables: temperature, relative humidity, and total cloud volume. We compared the performance of single models of machine and deep learning in previous work. For the single-model comparison, we used boosting techniques, such as extreme gradient boosting and categorical boosting (CatBoost) in machine learning, and the recurrent neural network (RNN) family (long short-term memory and gated recurrent units). In this paper, we compare CatBoost (previously the best model) with CNN and present a CNN-CatBoost hybrid model prediction method that combines CatBoost in machine learning and CNN in deep learning for the best predictive performance for a single-model comparison. In addition, we checked the accuracy change when adding wind speed and precipitation to the hybrid model. The model that considers wind speed and precipitation improved at all but three (Gangneung, Suwon, and Cheongju) of the 18 locations.INDEX TERMS Solar radiation forecasting, weather variable, categorical boosting (CatBoost), convolutional neural network (CNN), hybrid model (CNN-CatBoost)

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A deep learning based hybrid method for hourly solar radiation forecasting

Cited by 56 publications

References 41 publications

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection

Solar Radiation Forecasting Based on the Hybrid CNN-CatBoost Model

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